Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach

Int J Environ Res Public Health. 2022 Sep 2;19(17):11012. doi: 10.3390/ijerph191711012.

Abstract

Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.

Keywords: COVID-19; Delphi process; Portugal; dashboard; data visualization tools.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Data Visualization
  • Humans
  • Pandemics
  • Portugal / epidemiology

Grants and funding

This research was developed within the MSc thesis in Biomedical Engineering developed by Ekaterina Ignatenko at Instituto Superior Técnico (Universidade de Lisboa) and within the project SCOPE-Spatial Data Sciences for COVID-19 Pandemic (DSAIPA/DS/0115/2020). Manuel Ribeiro acknowledges Fundação para a Ciência e Tecnologia for the research contract IF2018/CP1384/IST-ID/175/2018. The first and third authors acknowledge support from the Centro de Estudos de Gestão of Instituto Superior Técnico, a research centre funded by the FCT project UIDB/00097/2020.